Traditionally, pedestrian movements are detected through step recognition. The aim of these methods is basically to determine the walking step length. Different researchers have used different methods to determine step-length, for instance modeling the step-length as a linear combination of step-frequency and accelerometer variance. Additionally, other methods have been based on determining the step-size based on the walking speed, using a second-order polynomial function, or using GPS positioning information for real-time step estimation using a Kalman filter.
The aforementioned methods fail to detect or differentiate movement styles performed by pedestrians such as walking, running, standing still, or any random movement that the user may perform. In applications, such as social gaming, or pure pedestrian navigation, knowing different movement phases of pedestrians may be valuable information.
Hence, it would be beneficial to detect walking steps as well as any movement that the user may perform in the context of social gaming.